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The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

When an electronic nose (e-nose) is used for prediction, extracting more useful information from the original response curve is of great importance. However, the most traditional feature extraction models in e-nose only sample a few data during the process of extracting features. To use more data and acquire more information to improve e-nose's classification accuracy, we present a new feature extraction method called ''weighted summation'' (WS). In addition, this method was compared with other exiting methods, including maximum value of the steady-state response (MAX), curve fitting (CF), dynamic moments of the phase space (MD2), maximum value of the first-order derivative (Dmax), and Db1 wavelet transformation (WT). Dingfeng pig farm located at Changchun (Jilin Province, China) was used as odor source. Four kinds of odors taken from inside of pig barn in the morning and in the evening, and outside of pig barn in the morning and in the evening were used as the original response of e-nose. The reasons why we choose these four classes are as follows: to start with, the smell of the house has a great influence on the health of pigs; then, outdoor odors affect residents' comfort level; and morning and evening are the most odorous hours. Experimental results demonstrated that for WS, MAX, CF, MD2, Dmax, and WT methods, accuracy in training set was 88.33%, 85%, 83.33%, 83.33%, 46.67%, and 51.67%, respectively, and accuracy in testing set was 100%, 100%, 91.67%, 91.67%, 41.67%, and 41.67%, respectively, suggesting that novel feature extraction method outperformed other methods. Moreover, a simple monitor system based on WS method was established to monitor the real environment in pig farm.INDEX TERMS Electronic nose, feature extraction, pig barn, weighted summation.

The composition of volatile organic compounds (VOCs) in large-scale livestock farms is complex, which seriously affects the health of livestock and is difficult to evaluate. In order to quickly analyze the pollution degree of VOCs in livestock farms, electronic nose technology was used in this study to detect and analyze the gases in pig and chicken houses, respectively. Firstly, the gas chromatography–mass spectrometry (GC–MS) and electronic nose were used to analyze the VOCs in the pig and chicken houses at different time and locations. The types and relative contents of VOCs were obtained from different livestock farms by GC–MS analysis. The sensor array response of the electronic nose showed similar results. In addition, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machine (SVM) analyses were performed on the electrical signal that was generated by the sensors of electronic nose, respectively. Finally, the classification rate of different odor sources in livestock farms was the highest (>85%), which indicates that SVM is a more effective method suitable for volatile gases recognition in livestock farms. The results have shown that the developed electronic nose sensor is a promising and feasible instrument for characterizing volatile odors in livestock farms.